Panic Propagation Dynamics of High-Density Crowd Based on Information Entropy and Aw-Rascle Model

Zhao, Rongyong; Hu, Qianshan; Liu, Qiong; Li, Cuiling; Dong, Daheng; Ma, Yunlong

doi:10.1109/tits.2019.2953357

Cited by 23 publications

(5 citation statements)

References 26 publications

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“…It is significant for passenger crowd safety and management to consider the spatial and temporal features of disturbance and the impact of disturbance on crowd stability. In our previous study, the panic propagation dynamics [26] and the disturbance propagation model for abnormal behavior of pedestrian were proposed [27]. In this study, we further propose the disturbance propagation model of the abnormal motions of luggage.…”

Section: Disturbance Propagation In Crowd Flowmentioning

confidence: 93%

Disturbance Propagation Model of Luggage Drifting Motion Based on Nonlinear Pressure in Typical Passenger Corridors of Transportation Hubs

Wei,

Zhao,

et al. 2024

Preprint

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In current transportation hubs, it is a common phenomenon that passengers travel with necessary wheeled-luggage or suitcases. Due to the fact that most luggage occupies certain space within dense passenger crowds with giant mass inertia, its abnormal motions, such as drifting, frequently trig unavoidable local disturbances and turbulence in surrounding pedestrian flows, further increasing security risk. While, current researches primarily concentrate on examining the impact of luggage on crowd evacuation efficiency through scenario-based experiments and analyzing the spatial characteristics of crowd distribution in surveillance videos. There still lacks of the dynamic disturbance mechanisms and crowd stability associated with the combined motion of passengers and luggage. Therefore, this study considers the luggage-laden passenger as a deformable particle, the resulting disturbance on surrounding non-luggage passengers is analyzed and quantified into a nonlinear pressure term. Subsequently, the disturbance propagation model of passenger-luggage is developed by adapting the classical Aw-Rascle traffic flow model with incorporation of the pressure term. Simulation experiments of disturbances caused by luggage drifting and retrograding validate that the proposed propagation model aligns with real-world situations in transportation hubs. Quantitively, when luggage-laden passenger drifted left, the disturbing force of left side crowd can reach a peak of 238N in the passenger density of 3.0 p/m2, and the maximum difference between the left and right sides disturbing force can reach 153N. Furthermore, it is recommended that the proposed model can be applied to crowd flow analysis and risk assessment.

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Section: Disturbance Propagation In Crowd Flowmentioning

confidence: 93%

Disturbance Propagation Model of Luggage Drifting Motion Based on Nonlinear Pressure in Typical Passenger Corridors of Transportation Hubs

Wei,

Zhao,

et al. 2024

Preprint

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“…We propose to use spatial-inner consistency (Ω sp ) to measure the uniformity degree within one region and spatialinter consistency (Γ sp ) to measure the similarity of the average velocities of two adjacent regions. The spatial-inner consistency is used to capture the internal disorganization, such as the case of escape in different directions (Zhao et al 2019). The spatial-inter consistency is used to evaluate the relationship between neighbor regions.…”

Section: Crowd Motion Consistency Representationmentioning

confidence: 99%

Crowd-Level Abnormal Behavior Detection via Multi-Scale Motion Consistency Learning

Luo

Yin³

et al. 2023

AAAI

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Detecting abnormal crowd motion emerging from complex interactions of individuals is paramount to ensure the safety of crowds. Crowd-level abnormal behaviors (CABs), e.g., counter flow and crowd turbulence, are proven to be the crucial causes of many crowd disasters. In the recent decade, video anomaly detection (VAD) techniques have achieved remarkable success in detecting individual-level abnormal behaviors (e.g., sudden running, fighting and stealing), but research on VAD for CABs is rather limited. Unlike individual-level anomaly, CABs usually do not exhibit salient difference from the normal behaviors when observed locally, and the scale of CABs could vary from one scenario to another. In this paper, we present a systematic study to tackle the important problem of VAD for CABs with a novel crowd motion learning framework, multi-scale motion consistency network (MSMC-Net). MSMC-Net first captures the spatial and temporal crowd motion consistency information in a graph representation. Then, it simultaneously trains multiple feature graphs constructed at different scales to capture rich crowd patterns. An attention network is used to adaptively fuse the multi-scale features for better CAB detection. For the empirical study, we consider three large-scale crowd event datasets, UMN, Hajj and Love Parade. Experimental results show that MSMC-Net could substantially improve the state-of-the-art performance on all the datasets.

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“…The degree of panic is measured through panic entropy. Numeric simulation is used to find the relationship between density and velocity of the crowd [268]. Similarly, Zhao et al modeled pedestrian merging dynamics at T-shaped street junctions using Aw-Rascle dynamic-flow model [269].…”

Section: G Crowd Managementmentioning

confidence: 99%

Solving Hajj and Umrah Challenges Using Information and Communication Technology: A Survey

Showail

2022

IEEE Access

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Hajj and Umrah are Muslims' religious pilgrimages to Makkah, Saudi Arabia. Hajj is considered the largest annual gathering in the world. In Islam, each and every able-bodied adult is obligated to go for Hajj at least once in his/her lifetime. Hajj takes place between the 8 th and the 13 th day of Dhul Hijjah, which is the last month of the Islamic year while Umrah, the minor pilgrimage, could be done any time of the year. In Hajj, pilgrims, also known as Hajjis, travel around 15 km and pilgrims have the choice to do it by foot, bus or by train. The Hajj journey starts from Mina, which is considered the largest tent city in the world because it hosts more than 10,000 fire resistant tents, and includes various religious rituals. Around 3 million Muslims from more than 183 countries make Hajj every year and the Kingdom of Saudi Arabia is working on increasing this number. Since 1989, researchers in various ICT domains have tried leveraging their expertise in solving challenges and issues related to Hajj and Umrah. Although several papers have surveyed efforts using technology in Hajj and Umrah research, none of them was comprehensive. In this paper, I classified research efforts that used information and communication technologies for solving Hajj and Umrah challenges over the past 33 years based on their applications in 10 categories. I also identified more than 30 technologies used by researchers all over the world to address Hajj and Umrah issues and group research efforts based on these technologies for ease of access. Furthermore, open challenges were discussed and new technologies that could be used to address these challenges were proposed.

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Panic Propagation Dynamics of High-Density Crowd Based on Information Entropy and Aw-Rascle Model

Cited by 23 publications

References 26 publications

Disturbance Propagation Model of Luggage Drifting Motion Based on Nonlinear Pressure in Typical Passenger Corridors of Transportation Hubs

Disturbance Propagation Model of Luggage Drifting Motion Based on Nonlinear Pressure in Typical Passenger Corridors of Transportation Hubs

Crowd-Level Abnormal Behavior Detection via Multi-Scale Motion Consistency Learning

Solving Hajj and Umrah Challenges Using Information and Communication Technology: A Survey

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